Tracking Small Animals in Complex Landscapes: A Comparison of Localisation Workflows for Automated Radio Telemetry Systems.

Automated radio telemetry systems (ARTS) have the potential to revolutionise our understanding of animal movement by providing a near-continuous record of individual locations in the wild. However, localisation errors in ARTS data can be very high, especially in natural landscapes with complex vegetation structure and topography. This curtails the research questions that may be addressed with this technology. We set up an ARTS grid in a valley with heterogeneous vegetation cover in the Colombian high Andes and applied an analytical pipeline to test the effectiveness of localisation methods. We performed calibration trials to simulate animal movement in high- or low-flight, or walking on the ground, and compared workflows with varying decisions related to signal cleaning, selection, smoothing, and interpretation, along with four multilateration approaches. We also quantified the influence of spatial features on the system's accuracy. Results showed large variation in localisation error, ranging between 0.4-43.4 m and 474-1929 m, depending on the localisation method used. We found that the selection of higher radio signal strengths and data smoothing based on the temporal autocorrelation are useful tools to improve accuracy. Moreover, terrain ruggedness, height of movement, vegetation type, and the location of animals inside or outside the grid area influence localisation error. In the case of our study system, thousands of location points were successfully estimated for two high-altitude hummingbird species that previously lacked movement data. Our case study on hummingbirds suggests ARTS grids can be used to estimate small animals' home ranges, associations with vegetation types, and seasonality in occurrence. We present a comparative localisation pipeline, highlighting the variety of possible decisions while processing radio signal data. Overall, this study provides guidance to improve the resolution of location estimates, broadening the application of this tracking technology in the study of the spatial ecology of wild populations.

Los sistemas de radio telemetría automatizada (ARTS, por sus siglas en inglés) tienen el potencial de revolucionar nuestro entendimiento de los movimientos de animales, ya que pueden generar registros de individuos en la naturaleza a escalas temporales muy finas. Sin embargo, el error de localización asociado a los datos generados por un sistema ARTS puede ser muy alto, especialmente en paisajes naturales con complejidad en su estructura vegetal y su topografía. Esto necesariamente limita las preguntas de investigación que se pueden abordar con dicha tecnología. En este estudio, instalamos una grilla ARTS en un valle con una cobertura vegetal heterogénea en ecosistemas altoandinos de Colombia, y evaluamos la efectividad de métodos de localización. Hicimos ensayos de calibración para simular el movimiento de animales tanto en vuelo alto y bajo como caminando sobre el piso, y comparamos procesos de localización con diferentes decisiones en los pasos de limpieza, selección, atenuación e interpretación de las señales de radio registradas. Además, probamos cuatro métodos de multilateración diferentes. También cuantificamos el efecto de características espaciales sobre la exactitud de la grilla ARTS. Los resultados muestran una gran variación en el error de localización, con coordenadas estimadas a sólo 0.4­43.4 m de la ubicación real hasta 474­1929 m, dependiendo del método de localización empleado. Encontramos que la selección de señales de radio más altas y la atenuación de los datos con base en la autocorrelación temporal de los datos de movimiento, son herramientas útiles para mejorar la exactitud del sistema. Además, la rugosidad del terreno, la altura del movimiento, el tipo de vegetación y la ubicación de los animales dentro o fuera de la grilla son variables que afectan el error de localización. Para nuestro caso de estudio, miles de puntos de ubicación fueron estimados para dos especies de colibríes que previamente no tenían datos de movimiento. Nuestro caso de estudio sobre colibríes de alta montaña sugiere que grillas ARTS son útiles para inferir rangos de hogar, asociaciones con tipos de vegetación y ocurrencia estacional de especies de animales pequeños. Presentamos una metodología comparativa de localización, resaltando la variedad de posibles decisiones durante el procesamiento de datos de señales de radio. Este estudio provee una guía para mejorar la resolución de estimados de ubicación, y amplía la aplicación de esta tecnología para el estudio de la ecología espacial de poblaciones silvestres.

Development of a novel Bluetooth Low Energy device for proximity and location monitoring in grazing sheep.

Monitoring animal location and proximity can provide useful information on behaviour and activity, which can act as a health and welfare indicator. However, tools such as global navigation satellite systems (GNSS) can be costly, power-hungry and often heavy, thus not viable for commercial uptake in small ruminant systems. Developments in Bluetooth Low Energy (BLE) could offer another option for animal monitoring, however, BLE signal strength can be variable, and further information is needed to understand the relationship between signal strength and distance in an outdoor environment and assess factors which might affect its interpretation in on-animal scenarios. A calibration of a purpose-built device containing a BLE reader, alongside commercial BLE beacons, was conducted in a field environment to explore how signal strength changed with distance and investigate whether this was affected by device height, and thus animal behaviour. From this calibration, distance prediction equations were developed whereby beacon distance from a reader could be estimated based on signal strength. BLE as a means of localisation was then trialled, firstly using a multilateration approach to locate 16 static beacons within an ∼5 400 m2 section of paddock using 6 BLE readers, followed by an on-sheep validation where two localisation approaches were trialled in the localisation of a weaned lamb within ∼1.4 ha of adjoining paddocks, surrounded by nine BLE readers. Validation was conducted using 1 days' worth of data from a lamb fitted with both a BLE beacon and separate GNSS device. The calibration showed a decline in signal strength with increasing beacon distance from a reader, with a reduced range and earlier decline in the proportion of beacons reported at lower reader and beacon heights. The distance prediction equations indicated a mean underestimation of 12.13 m within the static study, and mean underestimation of 1.59 m within the on-sheep validation. In the static beacon localisation study, the multilateration method produced a mean localisation error of 22.02 m, whilst in the on-sheep validation, similar mean localisation errors were produced by both methods - 19.00 m using the midpoint and 23.77 m using the multilateration method. Our studies demonstrate the technical feasibility of localising sheep in an outdoor environment using BLE technology; however, potential commercial application of such a system would require improvements in BLE range and accuracy.

Some Design Considerations in Passive Indoor Positioning Systems.

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Testing and Evaluation of Wi-Fi RTT Ranging Technology for Personal Mobility Applications.

The rapid growth in the technological advancements of the smartphone industry has classified contemporary smartphones as a low-cost and high quality indoor positioning tools requiring no additional infrastructure or equipment. In recent years, the fine time measurement (FTM) protocol, achieved through the Wi-Fi round trip time (RTT) observable, available in the most recent models, has gained the interest of many research teams worldwide, especially those concerned with indoor localization problems. However, as the Wi-Fi RTT technology is still new, there is a limited number of studies addressing its potential and limitations relative to the positioning problem. This paper presents an investigation and performance evaluation of Wi-Fi RTT capability with a focus on range quality assessment. A set of experimental tests was carried out, considering 1D and 2D space, operating different smartphone devices at various operational settings and observation conditions. Furthermore, in order to address device-dependent and other type of biases in the raw ranges, alternative correction models were developed and tested. The obtained results indicate that Wi-Fi RTT is a promising technology capable of achieving a meter-level accuracy for ranges both in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, subject to suitable corrections identification and adaptation. From 1D ranging tests, an average mean absolute error (MAE) of 0.85 m and 1.24 m is achieved, for LOS and NLOS conditions, respectively, for 80% of the validation sample data. In 2D-space ranging tests, an average root mean square error (RMSE) of 1.1m is accomplished across the different devices. Furthermore, the analysis has shown that the selection of the bandwidth and the initiator-responder pair are crucial for the correction model selection, whilst knowledge of the type of operating environment (LOS and/or NLOS) can further contribute to Wi-Fi RTT range performance enhancement.

Insights into the Conformational Plasticity of the Protein Kinase Akt1 by Multi-Lateral Dipolar Spectroscopy.

The serine/threonine kinase Akt1 is part of the PI3 K/Akt pathway and plays a key role in the regulation of various cellular processes such as cell growth, proliferation, and apoptosis. Here, we analyzed the elasticity between the two domains of the kinase Akt1, connected by a flexible linker, recording a wide variety of distance restraints by electron paramagnetic resonance (EPR) spectroscopy. We studied full length Akt1 and the influence of the cancer-associated mutation E17K. The conformational landscape in the presence of different modulators, like different types of inhibitors and membranes was presented, revealing a tuned flexibility between the two domains, dependent on the bound molecule.

Transmission Power Influence on WSN-Based Indoor Localization Efficiency.

Performing a good-quality indoor localization of a mobile target is a challenging task, which can be affected by many factors such as radio wave behavior, the nature of the experimental environment, and available infrastructure. (1) Background: An indoor localization experiment using an Internet of Things (IoT) wireless sensor network (WSN) testbed is performed, in order to study the influence of transmission power level on the quality of received signals, and consequently, the estimated target positional coordinates. (2) Methods: A received signal strength indicator (RSSI) range-based localization system using a geometrical constrained weighted least squares (WLS) estimator multilateration technique is selected to validate the influence of the transmission power level on the performance of the localization algorithm. (3) Results: Fair localization quality was obtained at the highest transmission power level instead of the proposed transmission power level. (4) Conclusion: Additional factors are discussed to fully represent the required operational transmission power for a better localization quality, along with suggested improvements of the infrastructure configuration as a future work.

Tracking protein domain movements by EPR distance determination and multilateration.

Electron paramagnetic resonance (EPR) spectroscopy in combination with site-directed spin labeling (SDSL) is a powerful approach to supplement the large toolbox of methods for protein structure determination. The strength of EPR spectroscopy is the ability to map flexibility of protein domains and conformational ensembles. EPR distance determination in the nanometer range and subsequent multilateration enables the three-dimensional visualization of the localization of a spin label in a protein domain. Therefore, multilateration can not only be used to represent the degree of flexibility of protein structural elements, but also track movements of whole domains. We report a detailed protocol for all needed steps, beginning with the choice of the labeling sites, the spin labeling reaction, the EPR distance measurement by double electron-electron resonance (DEER) and finally the multilateration exploiting the EPR distance restraints.

AutoLoc: Autonomous Sensor Location Configuration via Cross Modal Sensing.

Internet-of-Things (IoT) systems have become pervasive for smart homes. In recent years, many of these IoT sensing systems are developed to enable in-home long-term monitoring applications, such as personalized services in smart homes, elderly/patient monitoring, etc. However, these systems often require complicated and expensive installation processes, which are some of the main concerns affecting users' adoption of smart home systems. In this work, we focus on floor vibration-based occupant monitoring systems, which enables non-intrusive in-home continuous occupant monitoring, such as patient step tracking and gait analysis. However, to enable these applications, the system would require known locations of vibration sensors placed in the environment. Current practice relies on manually input of location, which makes the installation labor-intensive, time consuming, and expensive. On the other hand, without known location of vibration sensors, the output of the system does not have intuitive physical meaning and is incomprehensive to users, which limits the systems' usability. We present AutoLoc, a scheme to estimate the location of the vibration sensors in a two-dimensional space in the view of a nearby camera, which has spatial physical meaning. AutoLoc utilizes occupants' walking events captured by both vibration sensors and the co-located camera to estimate the vibration sensors' location in the camera view. First, AutoLoc detects and localizes the occupant's footsteps in the vision data. Then, it associates the time and location of the event to the floor vibration data. Next, the extracted vibration data of the given event from multiple vibration sensors are used to estimate the sensors' locations in the camera view coordinates. We conducted real-world experiments and achieved up to 0.07 meters localization accuracy.

Self-Localization of IoT Devices Using Noisy Anchor Positions and RSSI Measurements.

Location-enabled Internet of things (IoT) has attracted much attention from the scientific and industrial communities given its high relevance in application domains such as agriculture, wildlife management, and infectious disease control. The frequency and accuracy of location information plays an important role in the success of these applications. However, frequent and accurate self-localization of IoT devices is challenging due to their resource-constrained nature. In this paper, we propose a new algorithm for self-localization of IoT devices using noisy received signal strength indicator (RSSI) measurements and perturbed anchor node position estimates. In the proposed algorithm, we minimize a weighted sum-square-distance-error cost function in an iterative fashion utilizing the gradient-descent method. We calculate the weights using the statistical properties of the perturbations in the measurements. We assume log-normal distribution for the RSSI-induced distance estimates due to considering the log-distance path-loss model with normally-distributed perturbations for the RSSI measurements in the logarithmic scale. We also assume normally-distributed perturbation in the anchor position estimates. We compare the performance of the proposed algorithm with that of an existing algorithm that takes a similar approach but only accounts for the perturbations in the RSSI measurements. Our simulation results show that by taking into account the error in the anchor positions, a significant improvement in the localization accuracy can be achieved. The proposed algorithm uses only a single measurement of RSSI and one estimate of each anchor position. This makes the proposed algorithm suitable for frequent and accurate localization of IoT devices.

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System.

Ultrasonic local positioning systems (ULPS) have been brought to the attention of researchers as one of the possibilities that can be used for indoor localization. Acoustic systems combine a suitable trade-off between precision, ease of development, and cost. This work proposes a method for measuring the time of arrival of encoded emissions from a set of ultrasonic beacons, which are used to implement an accurate ULPS. This method uses the generalized cross-correlation technique with PHAT filter and weighting factor ß (GCC-PHAT-ß). To improve the performance of the GCC-PHAT-ß in encoded emission detection, the employment is proposed of mixed-medium multiple-access techniques, based on code division and time division multiplexing of beacon emissions (CDMA and TDMA respectively), and to dynamically adjust the PHAT filter weighting factor. The receiver position is obtained by hyperbolic multilateration from the time differences of arrival (TDoA) between a reference beacon and the rest, thus avoiding the need for receiver synchronization. The results show how the dynamic adaptation of the weighting factor significantly reduces positioning errors from 20 cm to 2 cm in 80% of measurements. The simulated and real experiments prove that the proposed algorithms improve the performance of the ULPS in situations with lower signal-to-noise ratios (SNR) than 0 dB and in environments where the multipath effect makes it difficult to correctly detect the encoded ultrasonic emissions.

Design of a Multi-Point Kinematic Coupling for a High Precision Telescopic Simultaneous Measurement System.

This paper covers the design of a new multi-point kinematic coupling specially developed for a high precision multi-telescopic arm measurement system for the volumetric verification of machine tools with linear and/or rotary axes. The multipoint kinematic coupling allows the simultaneous operation of the three telescopic arms that are registered at the same time to a sphere fixed on the machine tool spindle nose. Every coupling provides an accurate multi-point contact to the sphere, avoiding collisions and interferences with the other two multi-point kinematic couplings, and generating repulsion forces among them to ensure the coupling's fingers interlacing along the machine tool x/y/z travels in the verification process. Simulation presents minimal deformation of the kinematic coupling under load, assuring the precision of the sphere-to-sphere distance measurement. Experimental results are provided to show that the multi-point kinematic coupling developed has repeatability values below ±1.2 µm in the application.

Customer Segmentation through Path Reconstruction.

This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. A guess about the trajectory is constructed, and a number of parameters are calculated before performing a Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. We can also monitor the state of the shop, identify different situations that appear during limited periods of time, and predict peaks in customer traffic.

Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy.

Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m.

Improved Self-Calibration of a Multilateration System Based on Absolute Distance Measurement.

Multilateration tracking systems (MLTSs) are used in industrial three-dimensional (3D) coordinate measuring applications. For high-precision measurement, system parameters must be calibrated properly in advance. For an MLTS using absolute distance measurement (ADM), the conventional self-calibration method significantly reduces estimation efficiency because all system parameters are estimated simultaneously using a complicated residual function. This paper presents a novel self-calibration method that optimizes ADM to reduce the number of system parameters via highly precise and separate estimations of dead paths. Therefore, the residual function to estimate the tracking station locations can be simplified. By applying a suitable mathematical procedure and solving the initial guess problem without the aid of an external device, estimation accuracy of the system parameters is significantly improved. In three self-calibration experiments, with ADM repeatability of approximately 3.4 µm, the maximum deviation of the system parameters estimated by the proposed self-calibration method was 68.6 µm, while the maximum deviation estimated by the conventional self-calibration method was 711.9 µm. Validation of 3D coordinate measurements in a 1000 mm × 1000 mm × 1000 mm volume showed good agreement between the proposed ADM-based MLTS and a commercial laser tracker, where the maximum difference based on the standard deviation was 17.7 µm. Conversely, the maximum difference was 98.8 µm using the conventional self-calibration method. These results confirmed the efficiency and feasibility of the proposed self-calibration method.

Development of a High Precision Telescopic Instrument Based on Simultaneous Laser Multilateration for Machine Tool Volumetric Verification.

This paper presents the design of a high precision telescopic system consisting in three lines, with measuring principle based on simultaneous laser multilateration. The system offers the high precision of the interferometer systems and allows the autonomous tracking of a sphere joined to the spindle nose of the machine tool by simultaneous contact of all the lines. The main advantage of the system is that it allows data capture to be carried out in a single cycle thanks to simultaneous operation with at least three telescopic arms using a novel multipoint kinematic coupling. This results in a significant reduction of the time taken for data capture and improves measurement accuracy due to avoiding the effect of temperature variations between cycles and machine tool repeatability. The work explains the working principle of the system, its main components, and the design parameters considered for the development of the system. The system is simple to operate, compact, agile, and suitable for the verification of small- or medium-sized machine tools with linear and/or rotary axes.

Underwater Localization via Wideband Direction-of-Arrival Estimation Using Acoustic Arrays of Arbitrary Shape.

Underwater sensing and remote telemetry tasks necessitate the accurate geo-location of sensor data series, which often requires underwater acoustic arrays. These are ensembles of hydrophones that can be jointly operated in order to, e.g., direct acoustic energy towards a given direction, or to estimate the direction of arrival of a desired signal. When the available equipment does not provide the required level of accuracy, it may be convenient to merge multiple transceivers into a larger acoustic array, in order to achieve better processing performance. In this paper, we name such a structure an "array of opportunity" to signify the often inevitable sub-optimality of the resulting array design, e.g., a distance between nearest array elements larger than half the shortest acoustic wavelength that the array would receive. The most immediate consequence is that arrays of opportunity may be affected by spatial ambiguity, and may require additional processing to avoid large errors in wideband direction of arrival (DoA) estimation, especially as opposed to narrowband processing. We consider the design of practical algorithms to achieve accurate detections, DoA estimates, and position estimates using wideband arrays of opportunity. For this purpose, we rely jointly on DoA and rough multilateration estimates to eliminate spatial ambiguities arising from the array layout. By means of emulations that realistically reproduce underwater noise and acoustic clutter, we show that our algorithm yields accurate DoA and location estimates, and in some cases it allows arrays of opportunity to outperform properly designed arrays. For example, at a signal-to-noise ratio of -20 dB, a 15-element array of opportunity achieves lower average and median localization error (27 m and 12 m, respectively) than a 30-element array with proper λ / 2 element spacing (33 m and 15 m, respectively). We confirm the good accuracy of our approach via emulation results, and through a proof-of-concept lake experiment, where our algorithm applied to a 10-element array of opportunity achieves a 90th-percentile DoA estimation error of 4 ∘ and a 90th-percentile total location error of 5 m when applied to a real 10-element array of opportunity.

Characterization of an Ultrasonic Local Positioning System for 3D Measurements.

Indoor location and positioning systems (ILPS) are used to locate and track people, as well as mobile and/or connected targets, such as robots or smartphones, not only inside buildings with a lack of global navigation satellite systems (GNSS) signals but also in constrained outdoor situations with reduced coverage. Indoor positioning applications and their interest are growing in certain environments, such as commercial centers, airports, hospitals or factories. Several sensory technologies have already been applied to indoor positioning systems, where ultrasounds are a common solution due to its low cost and simplicity. This work proposes a 3D ultrasonic local positioning system (ULPS), based on a set of three asynchronous ultrasonic beacon units, capable of transmitting coded signals independently, and on a 3D mobile receiver prototype. The proposal is based on the aforementioned beacon unit, which consists of five ultrasonic transmitters oriented towards the same coverage area and has already been proven in 2D positioning by applying hyperbolic trilateration. Since there are three beacon units available, the final position is obtained by merging the partial results from each unit, implementing a minimum likelihood estimation (MLE) fusion algorithm. The approach has been characterized, and experimentally verified, trying to maximize the coverage zone, at least for typical sizes in most common public rooms and halls. The proposal has achieved a positioning accuracy below decimeters for 90% of the cases in the zone where the three ultrasonic beacon units are available, whereas these accuracies can degrade above decimeters according to whether the coverage from one or more beacon units is missing. The experimental workspace covers a large volume, where tests have been carried out at points placed in two different horizontal planes.

Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes.

Genomic global positioning system (GPS) applies the multilateration technique commonly used in the GPS to genomic data. In the framework we present here, investigators calculate genetic distances from their samples to reference samples, which are from data held in the public domain, and share this information with others. This sharing enables certain types of genomic analysis, such as identifying sample overlaps and close relatives, decomposing ancestry, and mapping of geographical origin without disclosing personal genomes. Thus, our method can be seen as a balance between open data sharing and privacy protection.

Self-Weighted Multilateration for Indoor Positioning Systems.

The paper proposes an improved method for calculating the position of a movable tag whose distance to a (redundant) set of fixed beacons is measured by some suitable physical principle (typically ultra wide band or ultrasound propagation). The method is based on the multilateration technique, where the contribution of each individual beacon is weighed on the basis of a recurring, self-supported calibration of the measurement repeatability of each beacon at a given distance range. The work outlines the method and its implementation, and shows the improvement in measurement quality with respect to the results of a commercial Ultra-Wide-Band (UWB) system when tested on the same set of raw beacon-to-tag distances. Two versions of the algorithm are proposed: one-dimensional, or isotropic, and 3D. With respect to the standard approach, the isotropic solution managed to reduce the maximum localization error by around 25%, with a maximum error of 0.60 m, while the 3D version manages to improve even further the localization accuracy, with a maximum error of 0.45 m.